import numpy as np
from sklearn.cluster import DBSCAN
import open3d as o3d
from pyod.models.lof import LOF
from torch_geometric.data import Data
from utils.project_settings import Classes
def polytopes_to_planes(pc):
pc[(pc[:, 6] == float(Classes.Box)) or (pc[:, 6] == float(Classes.Polytope)), 6] = float(Classes.Plane)
return pc
def mini_color_table(index, norm=True):
colors = [
[0.,0.,0.],
[0.5000, 0.5400, 0.5300], [0.8900, 0.1500, 0.2100], [0.6400, 0.5800, 0.5000],
[1.0000, 0.3800, 0.0100], [1.0000, 0.6600, 0.1400], [0.4980, 1.0000, 0.0000],
[0.4980, 1.0000, 0.8314], [0.9412, 0.9725, 1.0000], [0.5412, 0.1686, 0.8863],
[0.5765, 0.4392, 0.8588], [0.3600, 0.1400, 0.4300], [0.5600, 0.3700, 0.6000],
]
color = colors[index % len(colors)]
if not norm:
color[0] *= 255
color[1] *= 255
color[2] *= 255
return color
def cluster2Color(cluster, cluster_idx):
colors = np.zeros(shape=(len(cluster), 3))
point_idx = 0
for _ in cluster:
colors[point_idx, :] = mini_color_table(cluster_idx)
point_idx += 1
return colors
def label2color(labels):
'''
labels: np.ndarray with shape (n, )
colors(return): np.ndarray with shape (n, 3)
'''
num = labels.shape[0]
colors = np.zeros((num, 3))
minl, maxl = np.min(labels), np.max(labels)
for l in range(minl, maxl + 1):
colors[labels == l, :] = mini_color_table(l)
return colors
def read_pointcloud(path, delimiter=' ', hasHeader=True):
with open(path, 'r') as f:
if hasHeader:
# Get rid of the Header
_ = f.readline()
# This iterates over all lines, splits them and converts values to floats. Will fail on wrong values.
pc = [[float(x) for x in line.rstrip().split(delimiter)] for line in f if line != '']
return np.asarray(pc)[:, :6]
def write_pointcloud(file, pc, numCols=6):
np.savetxt(file, pc[:, :numCols], header=str(len(pc)) + ' ' + str(numCols), comments='')
def farthest_point_sampling(pts, K):
if K > 0:
if isinstance(pts, Data):
pts = pts.pos.numpy()
if pts.shape[0] < K:
return pts
else:
return pts
def calc_distances(p0, points):
return ((p0[:3] - points[:, :3]) ** 2).sum(axis=1)
farthest_pts = np.zeros((K, pts.shape[1]))
farthest_pts[0] = pts[np.random.randint(len(pts))]
distances = calc_distances(farthest_pts[0], pts)
for i in range(1, K):
farthest_pts[i] = pts[np.argmax(distances)]
distances = np.minimum(distances, calc_distances(farthest_pts[i], pts))
return farthest_pts
def cluster_per_column(pc, column):
clusters = []
for i in range(0, int(np.max(pc[:, column]))):
cluster_pc = pc[pc[:, column] == i, :]
clusters.append(cluster_pc)
return clusters
def cluster_cubes(data, cluster_dims, max_points_per_cluster=-1, min_points_per_cluster=-1):
if isinstance(data, Data):
import torch
candidate_list = list()
if data.pos is not None:
candidate_list.append(data.pos)
if data.norm is not None:
candidate_list.append(data.norm)
if data.y is not None:
candidate_list.append(data.y.double().unsqueeze(-1))
data = torch.cat(candidate_list, dim=-1).numpy()
if cluster_dims[0] == 1 and cluster_dims[1] == 1 and cluster_dims[2] == 1:
print("no need to cluster.")
return [farthest_point_sampling(data, max_points_per_cluster)]
max = data[:, :3].max(axis=0)
max += max * 0.01
min = data[:, :3].min(axis=0)
min -= min * 0.01
size = (max - min)
clusters = {}
cluster_size = size / np.array(cluster_dims, dtype=np.float32)
print('Min: ' + str(min) + ' Max: ' + str(max))
print('Cluster Size: ' + str(cluster_size))
for row in data:
# print('Row: ' + str(row))
cluster_pos = ((row[:3] - min) / cluster_size).astype(int)
cluster_idx = cluster_dims[0] * cluster_dims[2] * cluster_pos[1] + cluster_dims[0] * cluster_pos[2] + \
cluster_pos[0]
clusters.setdefault(cluster_idx, []).append(row)
# Apply farthest point sampling to each cluster
final_clusters = []
for key, cluster in clusters.items():
c = np.vstack(cluster)
if c.shape[0] < min_points_per_cluster and -1 != min_points_per_cluster:
continue
if max_points_per_cluster is not -1:
final_clusters.append(farthest_point_sampling(c, max_points_per_cluster))
else:
final_clusters.append(c)
return final_clusters
def cluster_dbscan(data, selected_indices, eps, min_samples=5, metric='euclidean', algo='auto'):
# print('Clustering. Min Samples: ' + str(min_samples) + ' EPS: ' + str(eps) + "Selected Indices: " + str(selected_indices))
# 0,1,2 : pos
# 3,4,5 : normal
# 6: type index
# 7,8,9,10: type index one hot encoded
# 11,12: normal as angles
db_res = DBSCAN(eps=eps, metric=metric, n_jobs=-1, min_samples=min_samples, algorithm=algo).fit(
data[:, selected_indices])
labels = db_res.labels_
n_clusters = len(set(labels)) - (1 if -1 in labels else 0)
n_noise = list(labels).count(-1)
# print("Noise: " + str(n_noise) + " Clusters: " + str(n_clusters))
clusters = {}
for idx, l in enumerate(labels):
if l == -1:
continue
clusters.setdefault(str(l), []).append(data[idx, :])
npClusters = []
for cluster in clusters.values():
npClusters.append(np.array(cluster))
return npClusters
def draw_clusters(clusters):
clouds = []
for cluster_idx, cluster in enumerate(clusters):
cloud = o3d.PointCloud()
cloud.points = o3d.Vector3dVector(cluster[:, :3])
cloud.colors = o3d.Vector3dVector(cluster2Color(cluster, cluster_idx))
clouds.append(cloud)
o3d.draw_geometries(clouds)
def write_clusters(path, clusters, type_column=6):
file = open(path, "w")
file.write(str(len(clusters)) + "\n")
for cluster in clusters:
# print("Types: ", cluster[:, type_column])
types = np.unique(cluster[:, type_column], axis=0).astype(int)
def type_mapping(t):
if t == 0:
return 2
elif t == 1:
return 1
elif t == 3:
return 4
return t
types = np.array([type_mapping(t) for t in types])
print("Types: {}, Points: {}".format(types, cluster.shape[0]))
# draw_sample_data(cluster)
np.savetxt(file, types.reshape(1, types.shape[0]), delimiter=';', header='', comments='', fmt='%i')
np.savetxt(file, cluster[:, :6], header=str(len(cluster)) + ' ' + str(6), comments='')
def draw_sample_data(sample_data, colored_normals=False):
cloud = o3d.PointCloud()
cloud.points = o3d.Vector3dVector(sample_data[:, :3])
cloud.colors = \
o3d.Vector3dVector(label2color(sample_data[:, 6].astype(int)) if not colored_normals else sample_data[:, 3:6])
o3d.draw_geometries([cloud])
def normalize_pointcloud(pc, factor=1.0):
max = pc.max(axis=0)
min = pc.min(axis=0)
f = np.max([abs(max[0] - min[0]), abs(max[1] - min[1]), abs(max[2] - min[2])])
pc[:, 0:3] /= (f * factor)
pc[:, 3:6] /= (np.linalg.norm(pc[:, 3:6], ord=2, axis=1, keepdims=True))
return pc
def hierarchical_clustering(data, selected_indices_0, selected_indices_1, eps, min_samples=5, metric='euclidean',
algo='auto'):
total_clusters = []
clusters = cluster_dbscan(data, selected_indices_0, eps, min_samples, metric=metric, algo=algo)
for cluster in clusters:
# cluster = normalize_pointcloud(cluster)
sub_clusters = cluster_dbscan(cluster, selected_indices_1, eps, min_samples, metric=metric, algo=algo)
total_clusters.extend(sub_clusters)
return total_clusters
def filter_clusters(clusters, filter):
filtered_clusters = []
for c in clusters:
if filter(c):
filtered_clusters.append(c)
return filtered_clusters
def split_outliers(pc, columns):
clf = LOF() # FeatureBagging() # detector_list=[LOF(), KNN()]
clf.fit(pc[:, columns])
# LOF, kNN
return pc[clf.labels_ == 0], pc[clf.labels_ == 1]
def append_onehotencoded_type(data, factor=1.0):
types = data[:, 6].astype(int)
res = np.zeros((len(types), 8))
res[np.arange(len(types)), types] = factor
return np.column_stack((data, res))